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Prediction of evaporation from dam reservoirs under climate change using soft computing techniques

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Abstract

This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein. In the first part, two machine learning models, namely group method of data handling (GMDH) and least squares support vector regression (LS-SVR), were used to model the inflow to the dam reservoir. Temperature, precipitation, and inflow during the previous month from 1990 to 2017 were used as input data. In the second part, the evaporation from the dam reservoir was modeled using the adaptive neuro-fuzzy inference system (ANFIS) and optimized ANFIS using Harris hawks optimization (HHO) and the arithmetic optimization algorithm (AOA) optimization algorithms. The input parameters in this part were temperature, precipitation, inflow to the dam reservoir, along with evaporation from the dam reservoir in the previous month. In the third part, precipitation and temperature were predicted using the fifth report of IPCC based on RCP2.6, RCP4.5, and RCP8.5 scenarios for the period 2020–2040. Out of 28 models presented in the fifth report, EC-ERATH and FIO-ESM had the greatest similarity with observational data of temperature and precipitation, respectively. The results of scatter plots and Taylor’s diagram showed the higher performance of LS-SVR (root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE) of 8.65, 4.69, and 0.96) compared to GMDH (RMSE, MAPE, and NSE of 11.65, 7.81, and 0.93) in modeling the inflow. Moreover, both hybrid modes (AOA-ANFIS and HHO-ANFIS) improved the performance of ANFIS in modeling the evaporation from the dam reservoir. The RMSE, MAPE, and NSE values for ANFIS were 0.56, 0.52, and 0.89, respectively, while these values for the AOA-ANFIS (RMSE, MAPE, and NSE of 0.31, 0.24, and 0.93) and HHO-ANFIS (RMSE, MAPE, and NSE of 0.20, 0.30, and 0.96) were improved remarkably. The impact of climate change reduced the inflow to the dam reservoir by about 0.45, 0.80, and 1.7 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Also, the effect of climate change caused the evaporation from the dam reservoir to increase by about 0.2, 0.9, and 1 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The findings of this study show that the correct management of dam reservoirs needs to consider the potential effects of climate change in the future. Moreover, the hybrid machine learning models used in this study can be used to predict the amount of evaporation in other reservoirs.

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Funding

This research is funded independently and supported by the authors.

Author information

Authors and Affiliations

  1. Department of Geology, Payame Noor University, Tehran, Iran

    Zahra Kayhomayoon

  2. Department of Water Engineering, College of Agricultural Sciences, University of Guilan, Rasht, Iran

    Fariba Naghizadeh

  3. Department of Civil Engineering, Azarshahr Branch, Islamic Azad University, Azarshahr, Iran

    Mohammadreza Malekpoor

  4. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Naser Arya Azar

  5. School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, Australia

    James Ball

  6. Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran

    Sami Ghordoyee Milan

Authors
  1. Zahra Kayhomayoon
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  2. Fariba Naghizadeh
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  3. Mohammadreza Malekpoor
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  4. Naser Arya Azar
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  5. James Ball
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  6. Sami Ghordoyee Milan
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Contributions

Z. Kayhomayoon: investigation, methodology, software, formal analysis, supervision, writing—original draft. F. Naghizadeh: methodology, software, visualization, writing—original draft. MR. Malekpoor: conceptualization, data curation, validation, writing—review and editing. N. Arya Azar: methodology, writing—original draft. J. Ball: supervision, writing—review and editing. S. Ghordoyee Milan: formal analysis, supervision, writing—review and editing.

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Correspondence to Zahra Kayhomayoon.

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Kayhomayoon, Z., Naghizadeh, F., Malekpoor, M. et al. Prediction of evaporation from dam reservoirs under climate change using soft computing techniques. Environ Sci Pollut Res 30, 27912–27935 (2023). https://doi.org/10.1007/s11356-022-23899-5

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